This summer, we were lucky enough to have Manchester students Rabia Butt and Klara Valentova joined us for a Q-step internship. Q-Step was developed as a strategic response to the shortage of quantitatively-skilled social science graduates. Manchester is one of 15 centres taking part in a £19.5 million programme designed to promote a step-change in quantitative social science training.
Here they review their time with us.
The purpose of our research was to explore deprivation in the UK using the Census data and the Carstairs Index of Deprivation.
To comprehend the overall transformation in the level of deprivation within the UK, the Census data from 1971 to the most recent one of 2011 was used in the calculation. In addition, different types of geographical levels, such as local authority, ward, lower super output areas, output area, and district were used for the calculation. This has now been accomplished. throughout this process, we learned many new techniques and skills, which we applied to achieve the final outcomes.
Our research has found that most of the extremely deprived areas are in cities.
To investigate why that might be the case, the individual scores of each of the indicators applied to produce the Carstairs scores were examined as they could explain what variable contributed to the specific level of deprivation the most.
It was found that in cities ‘Non-car ownership’ caused the final deprivation score to be so large. Owning a car in a city, however, can be very impractical, and so this suggests that the indicators used for the calculation of the Carstairs index are outdated and may need to be revised and possibly replaced with more relevant ones.
The most deprived areas overall are located in the City of London and Glasgow City, while the least deprived areas can be found in the suburbs of London, particularly in the southwest of London in towns such as Wokingham or cities such as St Albans.
That being said, our project has also found that some of the least deprived areas are also located in City of London and around Glasgow.
This came as a surprise since these two areas, in particular, seem to have to the highest levels of deprivation. This discovery was possible to made only due to the use of the Carstairs index as it allows for the analysis of smaller geographies such as output areas.
These results demonstrate that the Carstairs index is valuable in noticing small areas with high levels of deprivation be which would not be recognised as deprived when using other deprivation measures, for example the Indices of Multiple Deprivation.
We were also able to compare deprivation across different Census years for the whole of UK, and it was found that deprivation has decreased significantly between 1981 and 2011.
Deprivation in North Ireland, Scotland and Wales decreased the most, while in England this was the case for only certain areas. There was slight improvement for the south of England due to these areas always being less deprived. On the other hand, the north of England used to be greatly deprived especially in comparison to the South. Areas in the North have lowered their deprivation scores greatly, however, they still remain more deprived the South.
I have learnt a great deal during the process of our research, starting from not knowing much about Carstairs, R language, 3D mapping on QJIS, virtual reality and many more, to now having completed a research report.
I am most grateful for the experience I have gained during my internship and have not only improved on the skills I had, but also for acquiring new set of skills, which I will definitely be benefiting from in the near future.
One of the key skills which I have developed in is data analysis, especially when regarding quantitative data. This will definitely assist me in the final year of my degree, in the process of my dissertation.
Likewise, this internship has enhanced my knowledge of the role of data analyst, which was one of the reasons to why I wanted to do my internship here. This is because I wanted the experience of working for the UK Data Service to help me in determining the career path I wanted to take after I graduate.
I have enjoyed creating this project with a team member (Klara), as we were able to assist each other throughout the process in achieving the desired outcomes. This whole journey has definably helped me in my development, which has now sadly come to end.
I started this internship having basic data analysis skills, which I wanted to make use of and enhance them to a profession level. At the same time, I was hoping this experience would help me to decide what I would like to do after graduating, and whether data analysis and statistics would be something I would enjoy even outside of courses at the University. Working at the UK Data Service has fulfilled all the above.
I have learned many new skills and have developed on a professional level while discovering a whole new range of what I can do with data and its results (more about that in my previous blog at http://lab.ukdataservice.ac.uk/2018/08/21/klara-and-carstairs/).
Apart from developing knowledge of using different software such as QGIS or Microsoft Access, the one skill I developed the most was how to be successful when working in a team.
Having to work with another person, taking their idea on board and compromising has been very challenging for me yet incredibly rewarding. It was due to our excellent communication and mutual respect and understanding that we were able to calculate all the scores, do the analysis and finally produce the final report.
I have also discovered that I quite enjoyed guiding and helping Rabia throughout the internship, which helped me to gain very strong interpersonal skills that will be important in both my personal and professional life.
I am now very excited to have completed this project and to have gained so many invaluable skills and experiences. Nonetheless, I am sad to be leaving the UK Data Service as I enjoyed every task I had to complete, and I always felt very excited about working on this research. I am happy, however, that I now know I would like to do this type of job after finishing University because I enjoy it and find it very fulfilling and worthwhile.